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Patrick Ball wins the Karl E. Peace Award
Celebrating Women in Statistics
In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.
Mexico
The causal impact of bail on case outcomes for indigent defendants in New York City
Kristian Lum, Erwin Ma and Mike Baiocchi (2017). The causal impact of bail on case outcomes for indigent defendants in New York City. Observational Studies 3 (2017) 39-64. 31 October 2017. © 2017 Institute of Mathematical Statistics.
Collaboration between the Colombian Truth Commission, the Special Jurisdiction for Peace, and HRDAG (Dataset)
The Colombian Truth Commission (CEV), the Special Jurisdiction for Peace (JEP), and the Human Rights Data Analysis Group (HRDAG) have worked together to integrate data and calculate statistical estimates of the number of victims of the armed conflict, including homicides, forced disappearances, kidnapping, and the recruitment of child soldiers. Data are available through National Administrative Department of Statistics (DANE), the Truth Commission, and GitHub.
HRDAG and #GivingTuesday 2017
Quantifying Police Misconduct in Louisiana
Investigating Boston Police Department SWAT Raids from 2012 to 2020
HRDAG collaborated with Data for Justice Project on a tool tool allowing members of the public to visualize and analyze nearly a decade of Boston Police Department SWAT team after-action reports. Tarak Shah of HRDAG is named in the acknowledgments.
Drug-Related Killings in the Philippines
Patrick Ball, Sheila Coronel, Mariel Padilla and David Mora (2019). Drug-related killings in the Philippines. Human Rights Data Analysis Group. 26 July 2019. © HRDAG 2019.
How Data Scraping Provides Insights into Immigrant Arrests
Using MSE to Estimate Unobserved Events
A Pulitzer for an HRDAG partner
String matching for governorate information in unstructured text
The killings of social movement leaders and human rights defenders in Colombia 2018 – 2023: an estimate of the universe.
Valentina Rozo Ángel and Patrick Ball. 2024. The killings of social movement leaders and human rights defenders in Colombia 2018 – 2023: an estimate of the universe. Human Rights Data Analysis Group. 18 December 2024. © HRDAG 2024. Creative Commons International license 4.0.
Ciencia de datos para trazar un mapa de la crueldad a la mexicana
From the article: Esta entidad, que existe desde 1991, es liderada por su fundador, Patrick Ball, un científico que acumula una experiencia de más de 25 años realizando análisis cuantitativos en los lugares y en las situaciones más convulsos del planeta. Sobre su colaboración con el proyecto del predictor de fosas clandestinas en México, único en el mundo, Ball afirmó en entrevista:
“Cuando hablamos de crímenes de lesa humanidad estamos hablando de instituciones, de organizaciones grandes, cometiendo miles o centenares de miles de violaciones a víctimas distribuidas sobre una geografía enorme. Para entender los patrones en esas violaciones, la estadística puede brindar una mirada sobre quiénes son los responsables materiales e intelectuales, quiénes son las víctimas y dónde o cuándo pasaron esas violaciones. Pero la estadística no es contabilidad, pues no estamos hablando solamente de las violaciones que podemos ver, sino que también debemos calcular las violaciones no observadas, las escondidas, invisibles, para incluir en nuestro análisis la totalidad de las violaciones”.
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Data-Driven Efforts to Address Racial Inequality
From the article: “As we seek to advance the responsible use of data for racial injustice, we encourage individuals and organizations to support and build upon efforts already underway.” HRDAG is listed in the Data Driven Activism and Advocacy category.